Data analytics is shaping the future of banking, CIO News, ET CIO
Banks and Financial Institutions are investing heavily in enterprise intelligence solutions for data-driven decision-making and improving customer experience while capitalizing on cost-saving opportunities. Data analytics is fast emerging as one of the most important pillars of decision-making for the banking industry. The ability to churn real-time insights from customer data and get enhanced risk management are some of the factors driving spending on Big Data and Analytics (BDA) in the banking industry. As per an industry report, the global big data analytics market was valued at over 240 billion U.S. dollars in 2021. The market is expected to see significant growth over the coming years, with a forecasted market value of over 650 billion dollars by 2029.With data as the centerpiece of decision-making, banks are able to deliver more intelligent, personalized solutions and distinctive experiences at scale in real-time. AI, data analytics, and automation technologies are enabling banks to optimize and accelerate decision-making and also helping in smoothing their overall operations. Profit optimization with less resource allocation is the central feature of a bank’s business and data analytics as well as disruptive AI technologies can dramatically improve banks’ ability to achieve higher profits along with key outcomes such as at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles. Here are some examples of data permeating into the various facets of banking and shaping its future-
Platform operating model /digital operating models that fit the modern customer experience
Banks are moving steadily towards a platform operating model that recognizes the value of AI and technology for providing an enhanced customer and business experience. The traditional banking models which are generally slower with transactions taking several days for clearance of cheques and deposits to be completed are weakening as they are becoming less profit-generating and facing increasing competition from new players.
On the other hand, the platform operating modeloffersa unique advantage as it provides for business and technology partnerships- designed to focus on delivering cutting-edge AI-enabled solutions, that end up becoming an intrinsic component of the overall offering of the bank or financial services company.
Banking-as-a-platform (BaaP) and banking-as-a-service (BaaS)
Banking-as-a-platform (BaaP) and Banking-as-a-service (BaaS) are two platform-based models that are allowing banks to embrace transformation and deliver new products to more customers and geographies, quickly. While Banking-as-a-platform consists of offering new, third-party services that cover both financial and extra-financial products, in the Banking-as-a-service model a bank makes its core technologies and infrastructure available on a white-label basis via application programming interfaces (APIs).
With the Banking as a Service (BaaS) model banks can focus on their strengths and offer a wider range of services to customers. Also, by outsourcing non-core activities in the Banking as a platform (BaaP) model the maintenance costs are transferred to the developing company, which in turn helps the banks in reducing their development time & costs.
Better understanding of access risks through identity analytics
A typical Identity and Access Management (IAM) system contains basic information about users and what they can access. However, this data does not provide access-related risks. In order to obtain a holistic view of access risks, a bank must have information about what users are really doing with their access privileges.A modern approach to Identity and Access Management (IAM) requires the use of identity analytics, a process that employs big data, machine learning, and artificial intelligence (AI) technologies to analyze vast amounts of data and summarize it into actionable intelligence, allowing organizations to detect and respond to access risk more quickly.
Identity analytics makes Identity and Access Management (IAM) smarter by enhancing existing processes with a rich set of user activity and event data, peer group analysis, anomaly detection, and real-time monitoring and alerting which improves compliance and reduces risk. By incorporating AI technology, identity analytics becomes more robust and capable of automatically predicting trends and behaviors, and making recommendations for corrective action. AI uses data mining and machine learning techniques to generate hypotheses, evidence-based reasoning, and recommendations for improved decision-making in real-time.
Fraud detection and prevention with the help of advanced analytics
As online/internet banking is becoming increasingly popular, the instances of financial fraud are increasing year over year. According to the Anatomy of Fraud Report 2023 published by Praxis Global Alliance Ltd, Banking and financial services (BFSI) and e-commerce are the most vulnerable sectors with account-related frauds claiming a 65% share in financial services and a 54% share in e-commerce. Fraud analytics involves the use of big data analysis to prevent online financial fraud. It helps financial organizations predict future fraudulent behavior, and helps them apply fast detection and mitigation of fraudulent activity in real-time.
Since banks and other financial institutions are responsible to their customers to secure their data and finances against fraud, the deployment of fraud analytics can help banks prevent financial fraud and protect their customers’ assets more effectively than ever before.
Banks and Financial Institutions are looking for breakthroughs by identifying hidden opportunities with their data that can directly impact their bottom line. They are finding new ways to leverage data and predictive analytics to enhance the customer experience for business growth. Given the importance of data, it is not difficult to foresee banks of the future building their brands on a foundation of data where all the stages of the financial journey of a customer are captured to gain insights into the customer’s experience throughout their buying process.
The author is Ritesh Srivastava, Chief Data Scientist, BharatPe.
Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.
